PROJECT SUMMARY/ ABSTRACT Approximately 2 million Americans are living with an opioid use disorder, and in 2017, 17,029 people overdosed on prescription opioids. Opioids cause respiratory depression; however, fatal overdose usually occurs over a period of 1?3 hours, leaving a window of opportunity to intervene with the inexpensive drug naloxone, an opioid antagonist, that can reverse the effects of opioids, quickly restoring normal respiration rates. VivaLNK proposes to enhance the currently marketed Vital Scout wellness wearable sensing patch for use in people at risk for opioid overdose, by detecting physiologic indicators of an opioid overdose (slow respiration rate, low heart rate and low blood pressure), and the ability to send a potentially life-saving overdose alert, which could be received by an emergency contact/caregiver and hasten treatment with an opioid antagonist such as naloxone. Health monitoring emergency notification systems that can remotely monitor patient vital signs for indications of deterioration and send a potentially life-saving alert to emergency contacts/caregivers, have the potential to greatly reduce the number of fatal opioid overdoses in the US. The Vital Scout patch is already strategically positioned to monitor overdose parameters utilizing the built in ECG sensor to continuously register waveforms from which respiration rate, heart rate, heart rate variability, and activity are calculated. While several devices are currently being developed for overdose alerts, the Vital Scout is the only alert which has the potential to capture the three critical vital signs indicative of an overdose, and simultaneously monitor physical activity, providing additional context to the vital signs data. The vital signs collected by the Vital Scout patch are based on a machine-learning algorithm which we propose to further develop by the following specific aims: 1) Refinement and optimization of the Vital Scout to incorporate an overdose algorithm with an alert function by improving the sensitivity in order to detect lower respiration and heart rate indicative of a potential overdose. 2) Expand the sensing capabilities of the Vital Scout system to include blood pressure monitoring. The algorithm will be optimized using three volunteer cohorts (~20/ cohort) (i) healthy volunteers, (ii) elderly patients over the age of 65 (more likely to have low BP due to medically-induced hypotension) and (iii) low BP patients (represented by elite student-athletes). Data from these cohorts will provide the initial broad range of data for the machine-learning linear regression algorithm. We will assess whether these are appropriate designations for providing accurate blood pressure measurements, similar to what is seen within the opioid population. Meeting all of these goals will result in an enhanced Vital Scout that will be the most sensitive device for opioid overdose detection to remotely monitor the vital signs of people addicted to opioids and those at-risk for opioid overdose, including but not limited to, people with pulmonary disease or those taking antidepressants.